# Ear Challenge Eval

> Evaluates video action recognition models on classifying untrimmed real-world videos of elderly individuals into six daily activity categories. It probes robustness and generalization in wild, uncontrolled settings using a held-out test set. Use when the user wants to benchmark on EAR Challenge Test Set, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/ear-challenge-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ear-challenge-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ear-challenge-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ear-challenge-eval

---


# ear-challenge-eval

> Elderly Activity Recognition in the Wild: Results from the EAR Challenge — Duong (2025) (arXiv:2503.07821, 2025)

## What this evaluates

Evaluates video action recognition models on classifying untrimmed real-world videos of elderly individuals into six daily activity categories. It probes robustness and generalization in wild, uncontrolled settings using a held-out test set.

## Datasets

- **EAR Challenge Test Set** — total 4616; splits: test (4616)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Average accuracy of predictions on the test set, calculated as the number of correctly classified videos divided by the total number of videos.

## Input / output format

**Input**: Untrimmed video files representing daily activities of elderly individuals in real-world settings.

**Output**: A single class label from six predefined categories: locomotion, manipulation, hygiene, eating, communication, and leisure.

## Scoring recipe

```python
def calculate_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return correct / len(gold_labels)
```

## Common pitfalls

- The test set is untrimmed and unlabeled during the challenge, requiring models to handle variable-length videos without explicit temporal boundaries.
- Leaderboard rankings are determined solely by the private split (50% of test set), so public split performance may not reflect final ranking.
- Performance heavily depends on training duration; the authors note significant gains from 10 to 100 epochs, which may not be feasible under competition time limits.

## Evidence (verbatim from paper)

> The evaluation metric used for ranking submissions was the average accuracy of predictions on the test set.

## Citation

```bibtex
@misc{duong2025ear,
  title={Elderly Activity Recognition in the Wild: Results from the EAR Challenge},
  author={Duong (2025)},
  year={2025},
  note={arXiv:2503.07821}
}
```

- arXiv: 2503.07821